Project 2: Regression

Predicting the Fantasy Points in a subsequent game for a NFL Running Back (RB) and Wide Receiver (WR) based off of the statistics from the previous game.

Presentation Link: YouTube

Project Scope

Features (Independent Variables):

• RB: Carries/Game,Yards/Carry, Rushing Yards, Rushing Touchdowns, Receptions, Receiving Yards, Receiving Touchdowns, Fumbles
• WR: Receptions, Receiving Yards, Receiving Touchdowns, Carries/Game,Yards/Carry, Rushing Yards, Rushing Touchdowns, Fumbles

Dependent Variable (Target): Fantasy Football Points for next game

Data

• Analyze data from 2019 season from Pro-Football-Reference (https://www.pro-football-reference.com/)
• Running Backs and Wide Receivers around 500 Rushing/Receiving Yards and above

Methodology

Apply all regression models as aforementioned and compare Train, Validation, and Test R^2 values to determine best for model. Utilize other regression models such as LASSO and Ridge to Feature Engineer. Create running average of statistics for entire 2019 season (ie.: If Game 3 and predicting Fantasy Points for Game 4, then Game 3 statistics will be an average of games 1, 2, and 3.)

Deliverable

Present model with the best RSME score to determine how many points model comes within actual score. Reduce RSME as much as possible to create most accurate model.

Future Iterations

Aggregate Features on 4-week running averages (ie.: Game 5 features predicting Fantasy Points for Game 6, average statistics from Games 2,3,4, and 5)

Target Audience

• Fantasy Football Enthusiasts

Skills:

• basics of the web (requests, HTML, CSS, JavaScript)
• BeautifulSoup (web scraping)
• numpy, pandas, Jupyter Notebooks
• statsmodels, scikit-learn
• Seaborn
• Matplotlib
• Yellowbrick